Focusing on the shortages of existing ciphertext retrieval schemes in cloud computing, such as not supporting semantic extension of retrieval keyword, low accuracy and not ranking search results, a ranked ciphertext retrieval scheme supporting semantic extension of retrieval keyword was proposed. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) method was used to calculate the relevance scores between keywords and documents, and different weights were set for keywords in different document domains. The position weight scores of keywords in different document domains were calculated based on domain-weighted scoring method. The value of keyword corresponding position on document index vector was set as the product of position weight score and relevance score. Secondly, according to WordNet semantic Web, semantic extension was performed on retrieval keywords that input by the authorized users, and edit distance formula was used to calculate the similarity among semantic extension keywords, and the value of retrieval keyword corresponding position on document retrieval vector was set as similarity value. Finally, security index and document retrieval trapdoors were generated by encryption, and the inner product operation was performed based on Vector Space Model (VSM), and the result of ciphertext retrieval documents was sorted by the value of inner product operation. The theoretical analysis and experimental simulations show that the proposed scheme is safe under the known ciphertext model and the known background knowledge model, and has the ability to sort the search results. Compared with Multi-keyword Ranked Search over Encrypted cloud data (MRSE) scheme, the proposed scheme supports keyword semantic extension, and is more accurate and reliable than MRSE, while the retrieval time is not much different from MRSE scheme.

It is difficult to retrieve ciphertext in cloud computing. Existing searchable encryption schemes have low time efficiency, which file retrieval index does not support update, and retrieval results cannot be sorted accurately. To solve these problems, firstly, file retrieval index was constructed based on counting Bloom filter, through hash mapping files keywords to counting Bloom filter index vector, to realize ciphertext retrieval with keywords, and support updating of the ciphertext retrieval index. Secondly, because the counting Bloom filter does not have semantic function, it cannot achieve the ranking of retrieval results according to the relevance scores of the keywords. in this paper, the relevance scores of keywords were computed by using keyword frequency matrix and Term Frequency-Inverse Document Frequency (TF-IDF) model, to achieve the ranking function of retrieval results with the relevance score. Finally, theoretical and experimental performance analysis show that, this proposed method is secure, updatable, sortable, and efficient.